def test_spm_hrf():
# set the TR length ... this affects the HRF sampling rate ...
tr_length = 1.0
# compute the difference in area under curve for hrf_delays of -1 and 0
diff_1 = np.abs(np.sum(utils.spm_hrf(-1, tr_length))-np.sum(utils.spm_hrf(0, tr_length)))
# compute the difference in area under curver for hrf_delays of 0 and 1
diff_2 = np.abs(np.sum(utils.spm_hrf(1, tr_length))-np.sum(utils.spm_hrf(0, tr_length)))
npt.assert_almost_equal(diff_1, diff_2, 2)
def test_spm_hrf():
# set the TR length ... this affects the HRF sampling rate ...
tr_length = 1.0
# compute the difference in area under curve for hrf_delays of -1 and 0
diff_1 = np.abs(
np.sum(utils.spm_hrf(-1, tr_length)) -
np.sum(utils.spm_hrf(0, tr_length)))
# compute the difference in area under curver for hrf_delays of 0 and 1
diff_2 = np.abs(
np.sum(utils.spm_hrf(1, tr_length)) -
np.sum(utils.spm_hrf(0, tr_length)))
npt.assert_almost_equal(diff_1, diff_2, 2)
prf_dm = prf_dm.T # then it'll be (x, y, t)
# change DM to see if fit is better like that
# do new one which is average of every 2 TRs
prf_dm = shift_DM(prf_dm)
prf_dm = prf_dm[:, :, analysis_params[
'crop_pRF_TR']:] # crop DM because functional data also cropped now
# define model params
fit_model = analysis_params["fit_model"]
TR = analysis_params["TR"]
hrf = utilities.spm_hrf(0, TR)
# make stimulus object, which takes an input design matrix and sets up its real-world dimensions
prf_stim = PRFStimulus2D(
screen_size_cm=analysis_params["screen_width"],
screen_distance_cm=analysis_params["screen_distance"],
design_matrix=prf_dm,
TR=TR)
# sets up stimulus and hrf for this gridder
gg = Iso2DGaussianGridder(
stimulus=prf_stim,
hrf=hrf,
filter_predictions=False,
window_length=analysis_params["sg_filt_window_length"],
polyorder=analysis_params["sg_filt_polyorder"],
dms = []
for ix, row in df.iterrows():
dm = create_visual_designmatrix_all(
bar_width=0.125,
iti_duration=0,
thetas=[row.direction],
n_pixels=n_pixels,
nr_timepoints=row.bar_pass_duration
) # Note that we make a design matrix at level of seconds
dms.append(dm)
dm = np.concatenate(dms, -1)
stimulus = VisualStimulus(dm, distance_screen, size_cm, 0.50, 1.0,
ctypes.c_int16)
hrf = utils.spm_hrf(0, 1.)
bold_dm = fftconvolve(stimulus.stim_arr, hrf[np.newaxis, np.newaxis, :],
'full')
bold_dm = np.moveaxis(bold_dm, -1, 0)[:total_duration]
bold_dm = resample(bold_dm, int(total_duration / TR), axis=0)
np.savetxt('/data/odc/derivatives/prf/dm.txt', dm.reshape(-1, n_pixels**2))
np.savetxt('/data/odc/derivatives/prf/bold_dm.txt',
bold_dm.reshape(-1, n_pixels**2))
np.save('/data/odc/derivatives/prf/dm.npy', dm)